{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T18:16:08Z","timestamp":1787940968927,"version":"build-2784847793"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"content-version":"vor","delay-in-days":18,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Andrew Sisson Support Package for Early Career Researchers"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-2113404"],"award-info":[{"award-number":["DMS-2113404"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-2515765"],"award-info":[{"award-number":["DMS-2515765"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UNC Computational Medicine Program Pilot Award"},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"crossref","award":["GNT2025648"],"award-info":[{"award-number":["GNT2025648"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Multiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present an open-source R package implementing data integration via analysis of subspaces (DIVAS), a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas existing methods did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>DIVAS is available at https:\/\/github.com\/ByronSyun\/DIVAS, with documentation and vignettes. The COVID-19 case study vignette is available at https:\/\/byronsyun.github.io\/DIVAS_COVID19_CaseStudy\/.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag615","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T11:17:24Z","timestamp":1786965444000},"source":"Crossref","is-referenced-by-count":0,"title":["DIVAS: an R package for identifying shared and individual variations of multiomics data"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8600-2520","authenticated-orcid":false,"given":"Yinuo","family":"Sun","sequence":"first","affiliation":[{"name":"Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne , Parkville, Victoria 3010,","place":["Australia"]},{"name":"Department of Biochemistry and Molecular Biology, Cancer and Infection Programs, Biomedicine Discovery Institute, Monash University , Clayton, Victoria 3800,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2000-1476","authenticated-orcid":false,"given":"James Stephen","family":"Marron","sequence":"additional","affiliation":[{"name":"Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill , Chapel Hill, NC 27599,","place":["United States"]},{"name":"School of Data Science and Society, The University of North Carolina at Chapel Hill , Chapel Hill, NC 27599,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3923-1116","authenticated-orcid":false,"given":"Kim-Anh","family":"L\u00ea Cao","sequence":"additional","affiliation":[{"name":"Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne , Parkville, Victoria 3010,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3818-1981","authenticated-orcid":false,"given":"Jiadong","family":"Mao","sequence":"additional","affiliation":[{"name":"Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne , Parkville, Victoria 3010,","place":["Australia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,18]]},"reference":[{"key":"2026082813163018700_btag615-B1","first-page":"1","article-title":"Multifaceted neuroimaging data integration via analysis of subspaces","author":"Ackerman","year":"2025","journal-title":"Psychometrika"},{"key":"2026082813163018700_btag615-B2","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1038\/s41587-022-01520-x","article-title":"Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models","volume":"41","author":"Alles\u00f8e","year":"2023","journal-title":"Nat Biotechnol"},{"key":"2026082813163018700_btag615-B3","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s13059-020-02015-1","article-title":"MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data","volume":"21","author":"Argelaguet","year":"2020","journal-title":"Genome Biol"},{"key":"2026082813163018700_btag615-B4","doi-asserted-by":"crossref","first-page":"e8124","DOI":"10.15252\/msb.20178124","article-title":"Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets","volume":"14","author":"Argelaguet","year":"2018","journal-title":"Mol Syst Biol"},{"key":"2026082813163018700_btag615-B5","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1038\/s41592-023-01909-9","article-title":"MultiVI: deep generative model for the integration of multimodal data","volume":"20","author":"Ashuach","year":"2023","journal-title":"Nat Methods"},{"key":"2026082813163018700_btag615-B6","author":"Carmichael","year":"2020"},{"key":"2026082813163018700_btag615-B7","doi-asserted-by":"crossref","first-page":"e24257","DOI":"10.1002\/jcla.24257","article-title":"Kynurenine pathway in coronavirus disease (COVID-19): potential role in prognosis","volume":"36","author":"Cihan","year":"2022","journal-title":"J Clin Lab Anal"},{"key":"2026082813163018700_btag615-B8","doi-asserted-by":"crossref","first-page":"eabl5197","DOI":"10.1126\/science.abl5197","article-title":"Cross-tissue immune cell analysis reveals tissue-specific features in humans","volume":"376","author":"Dom\u00ednguez Conde","year":"2022","journal-title":"Science"},{"key":"2026082813163018700_btag615-B9","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.jmva.2018.03.008","article-title":"Angle-based joint and individual variation explained","volume":"166","author":"Feng","year":"2018","journal-title":"J. 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